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Publication Detail
Integrative biology approach identifies cytokine targeting strategies for psoriasis.
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Journal Article
  • Authors:
    Perera GK, Ainali C, Semenova E, Hundhausen C, Barinaga G, Kassen D, Williams AE, Mirza MM, Balazs M, Wang X, Rodriguez RS, Alendar A, Barker J, Tsoka S, Ouyang W, Nestle FO
  • Publication date:
  • Pagination:
    223ra22, ?
  • Journal:
    Sci Transl Med
  • Volume:
  • Issue:
  • Status:
  • Country:
    United States
  • PII:
  • Language:
  • Keywords:
    Animals, Antibodies, Monoclonal, Disease Models, Animal, Humans, Interleukins, Mice, Mice, Knockout, Psoriasis
Cytokines are critical checkpoints of inflammation. The treatment of human autoimmune disease has been revolutionized by targeting inflammatory cytokines as key drivers of disease pathogenesis. Despite this, there exist numerous pitfalls when translating preclinical data into the clinic. We developed an integrative biology approach combining human disease transcriptome data sets with clinically relevant in vivo models in an attempt to bridge this translational gap. We chose interleukin-22 (IL-22) as a model cytokine because of its potentially important proinflammatory role in epithelial tissues. Injection of IL-22 into normal human skin grafts produced marked inflammatory skin changes resembling human psoriasis. Injection of anti-IL-22 monoclonal antibody in a human xenotransplant model of psoriasis, developed specifically to test potential therapeutic candidates, efficiently blocked skin inflammation. Bioinformatic analysis integrating both the IL-22 and anti-IL-22 cytokine transcriptomes and mapping them onto a psoriasis disease gene coexpression network identified key cytokine-dependent hub genes. Using knockout mice and small-molecule blockade, we show that one of these hub genes, the so far unexplored serine/threonine kinase PIM1, is a critical checkpoint for human skin inflammation and potential future therapeutic target in psoriasis. Using in silico integration of human data sets and biological models, we were able to identify a new target in the treatment of psoriasis.
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